A simple method reveals minimum time required to quantify steady-rate metabolism and net cost of transport for human walking

James-Stein Estimator Improves Accuracy and Sample Efficiency in Human Kinematic and Metabolic Data

Human biomechanical data are often accompanied with measurement noise and behavioral variability. Errors due to such noise and variability are usually exaggerated by fewer trials or shorter trial durations and could be reduced using more trials or longer trial durations. Speeding up such data collection by lowering number of trials or trial duration, while improving the accuracy of statistical estimates, would be of particular interest in wearable robotics applications and when the human population studied is vulnerable (e.g., the elderly). Here, we propose the use of the James-Stein estimator (JSE) to improve statistical estimates with a given amount of data or reduce the amount of data needed for a given accuracy. The JSE is a shrinkage estimator that produces a uniform reduction in the summed squared errors when compared with the more familiar maximum likelihood estimator (MLE), simple averages, or other least squares regressions. When data from multiple human participants are available, an individual participant's JSE can improve upon MLE by incorporating information from all participants, improving overall estimation accuracy on average. Here, we apply the JSE to multiple time series of kinematic and metabolic data from the following parameter estimation problems: foot placement control during level walking, energy expenditure during circle walking, and energy expenditure during resting. We show that the resulting estimates improve accuracy-that is, the James-Stein estimates have lower summed squared error from the 'true' value compared with more conventional estimates.

Measuring metabolic energy expenditure with short duration walking tests for individuals with lower limb amputation

INTRODUCTION

Metabolic assessment of prosthetic gait is useful when comparing devices, interventions, or populations. However, the standard requirement to walk continuously for six minutes or more to reach steady state (SS) is difficult for many individuals with lower limb amputation. Our goal was to assess the concurrent validity of metabolic outcomes from shorter duration walking tests with those from the standard six-minute walk, in persons with transfemoral or transtibial amputation.

METHODS

Thirty participants (amputation: 10 transfemoral, 10 transtibial, 10 none) performed three walking tests while data were collected with a wearable metabolic system: 1) two-minute treadmill walk plus 10-minute recovery, 2) six-minute treadmill walk, and 3) overground two-minute walk test (2MWT). Three different analyses were performed to correlate SS metabolic outcomes from minutes 5-6 of the six-minute treadmill walk with: 1) total oxygen uptake from the two-minute treadmill walk, incorporating excess post-exercise oxygen consumption (EPOC), 2) minute interval outcomes from minutes 1-4 of the six-minute treadmill walk, and 3) outcomes during minutes 1 and 2 of the 2MWT.

RESULTS

Strong correlations were found between total oxygen uptake of the two-minute treadmill walk plus EPOC and SS oxygen uptake (Pearson r 0.86 to 0.94). Likewise, there were strong correlations between minute interval outcomes of minutes 2, 3, and 4 of the six-minute treadmill walk and SS outcomes (Pearson r 0.82 to > 0.99). Fewer significant correlations were observed when comparing 2MWT outcomes with SS outcomes (Pearson r 0.41 to 0.78).

CONCLUSION

Strong correlations between metabolic outcomes of shorter duration walking tests with SS outcomes suggest that treadmill walking tests as short as two minutes may be acceptable to compare energy expenditure between conditions in individuals with lower limb amputation for circumstances where longer duration tests would not be possible. Additionally, these shorter tests would be more similar to real-life activities and more accessible for those with lower limb amputation.

James-Stein estimator improves accuracy and sample efficiency in human kinematic and metabolic data

Human biomechanical data are often accompanied with measurement noise and behavioral variability. Errors due to such noise and variability are usually exaggerated by fewer trials or shorter trial durations, and could be reduced using more trials or longer trial durations. Speeding up such data collection by lowering number of trials or trial duration, while improving the accuracy of statistical estimates, would be of particular interest in wearable robotics applications and when the human population studied is vulnerable (e.g., the elderly). Here, we propose the use of the James-Stein estimator (JSE) to improve statistical estimates with a given amount of data, or reduce the amount of data needed for a given accuracy. The JSE is a shrinkage estimator that produces a uniform reduction in the summed squared errors when compared to the more familiar maximum likelihood estimator (MLE), simple averages, or other least squares regressions. When data from multiple human participants are available, an individual participant's JSE can improve upon MLE by incorporating information from all participants, improving overall estimation accuracy on average. Here, we apply the JSE to multiple time-series of kinematic and metabolic data from the following parameter estimation problems: foot placement control during level walking, energy expenditure during circle walking, and energy expenditure during resting. We show that the resulting estimates improve accuracy - that is, the James-Stein estimates have lower summed squared error from the 'true' value compared to more conventional estimates.

Perturbation-based estimation of within-stride cycle metabolic cost

Metabolic cost greatly impacts trade-offs within a variety of human movements. Standard respiratory measurements only obtain the mean cost of a movement cycle, preventing understanding of the contributions of different phases in, for example, walking. We present a method that estimates the within-stride cost of walking by leveraging measurements under different force perturbations. The method reproduces time series with greater consistency (r = 0.55 and 0.80 in two datasets) than previous model-based estimations (r = 0.29). This perturbation-based method reveals how the cost of push-off (10%) is much smaller than would be expected from positive mechanical work (~ 70%). This work elucidates the costliest phases during walking, offering new targets for assistive devices and rehabilitation strategies.

Exploring How the Arms Can Help the Legs in Facilitating Gait Rehabilitation

Inspired by the ideas from the fields of gait rehabilitation, neuroscience, and locomotion biomechanics and energetics, a body of work is reviewed that has led to propose a conceptual framework for novel "self-assistive" walking devices that could further promote walking recovery from incomplete spinal cord injuries. The underlying rationale is based on a neural coupling mechanism that governs the coordinated movements of the arms and legs during walking, and that the excitability of these neural pathways can be exploited by actively engaging the arms during locomotor training. Self-assistive treadmill walking rehabilitation devices are envisioned as an approach that would allow an individual to actively use their arms to help the legs during walking. It is hoped that the conceptual framework inspires the design and use of self-assistive walking devices that are tailored to assist individuals with an incomplete spinal cord injury to regain their functional walking ability.

Human-in-the-Loop Optimization of Knee Exoskeleton Assistance for Minimizing User's Metabolic and Muscular Effort

Lower limb exoskeletons have the potential to mitigate work-related musculoskeletal disorders; however, they often lack user-oriented control strategies. Human-in-the-loop (HITL) controls adapt an exoskeleton's assistance in real time, to optimize the user-exoskeleton interaction. This study presents a HITL control for a knee exoskeleton using a CMA-ES algorithm to minimize the users' physical effort, a parameter innovatively evaluated using the interaction torque with the exoskeleton (a muscular effort indicator) and metabolic cost. This work innovates by estimating the user's metabolic cost within the HITL control through a machine-learning model. The regression model estimated the metabolic cost, in real time, with a root mean squared error of 0.66 W/kg and mean absolute percentage error of 26% (n = 5), making faster (10 s) and less noisy estimations than a respirometer (K5, Cosmed). The HITL reduced the user's metabolic cost by 7.3% and 5.9% compared to the zero-torque and no-device conditions, respectively, and reduced the interaction torque by 32.3% compared to a zero-torque control (n = 1). The developed HITL control surpassed a non-exoskeleton and zero-torque condition regarding the user's physical effort, even for a task such as slow walking. Furthermore, the user-specific control had a lower metabolic cost than the non-user-specific assistance. This proof-of-concept demonstrated the potential of HITL controls in assisted walking.

Metabolic power response to added mass on the lower extremities during running

BACKGROUND

Wearable exoskeletal devices can enhance locomotor performance, but their mass results in a metabolic penalty. Previous studies have quantified the metabolic cost of running with added mass on the feet, but less is known about the effects of adding mass to the thigh and shank segments.

AIM

To quantify the metabolic cost of running with additional leg mass.

METHODS

15 participants (7 F, 8 M) completed treadmill running trials (3 m/s) normally and with lead mass (300-1350 g) attached to either the thigh, shank, or foot, bilaterally. We measured metabolic power using expired gas analysis.

RESULTS

Per 1000 g of added mass per leg, gross metabolic power increased by approximately 16% (foot) and 11% (shank) for females which was slightly greater than the 11% and 8% increases for males, respectively. For thigh loading, metabolic power increased by just 4% per 1000 g in both sexes.

CONCLUSION

Adding mass more distally on the leg increases the metabolic cost of running to a greater extent. For the same absolute added mass on the foot or shank, metabolic power increases more in females.